Advanced Confident Learning and Applications for GenAI

Advanced Confident Learning and Applications for GenAI #

Q1: What is the main focus of this lecture? #

  • Advanced Confident Learning (CL): Theory, methods, and applications, especially for Generative AI (images, text).

Q2: How does Confident Learning (CL) work at its core? #

  • Inputs: Noisy labels and predicted probabilities.
  • Core idea: Find self-confidence thresholds per class to detect label errors.
  • Estimate if an example is an error, correct label, or outlier.

Q3: What is the quick intuition behind CL? #

  • Off-diagonal entries in the predicted-vs-true label matrix reveal label errors.

Q4: What makes CL robust to noise? #

  • Prune principle: Remove low-confidence errors before training.
  • Count principle: Use counts rather than raw outputs.
  • Rank principle: Rank by model confidence, not rely on probabilities.

Q5: How is CL better than just loss adjustment techniques? #

  • CL avoids error propagation common in reweighting methods.
  • Robust to stochastic/noisy outputs from real-world models.

Q6: What is the theoretical guarantee of CL? #

  • As long as correct labels dominate wrong ones in a class, CL can exactly find errors — even if model probabilities are imperfect (up to ~33% wrong).

Q7: Why does label noise in test sets matter? #

  • 3.4% of labels in popular ML test sets are wrong.
  • Small label error rates (~6%) can change model rankings drastically.
  • Benchmark results can be misleading without corrected test sets.

Q8: How to fix label errors in test sets? #

  • Use majority consensus among reviewers to correct labels.
  • Prune uncertain/multi-label examples.

Q9: How is CL applied to Generative AI models? #

  • Before training: Clean training data to avoid issues in model generation.
  • After generation: Run CL on generated data (e.g., images/text) to remove/fix errors.

Q10: Example use cases for CL in Generative AI? #

Scenario Application
Image generation (e.g., DALL-E) Improve datasets pre/post generation
LLM outputs (e.g., GPT-4) Post-process outputs for better quality
RAG (Retrieval-Augmented Generation) Clean retrieved answers
Trustworthy Language Models (TLM) Attach confidence scores to outputs

Q11: Final Takeaways #

  • CL is model-agnostic.
  • Improves reliability of both traditional ML models and Generative AI.
  • One line of code to apply using cleanlab.

References #